Hybrid Approach for Orientation-Estimation of Rotating Humans in Video Frames Acquired by Stationary Monocular Camera

Show simple item record
dc.contributor.authorBaumgartner, David
dc.contributor.authorZucali, Tobias
dc.contributor.authorZwettler, Gerald A.
dc.contributor.editorSkala, Václav
dc.date.accessioned2020-07-27T10:34:45Z
dc.date.available2020-07-27T10:34:45Z
dc.date.issued2020
dc.description.abstract-translatedThe precise orientation-estimation of humans relative to the pose of a monocular camera system is a challenging task due to the general aspects of camera calibration and the deformable nature of a human body in motion. Thus, novel approaches of Deep Learning for precise object pose-estimation in robotics are hard to adapt to human body analysis. In this work, a hybrid approach for the accurate estimation of a human body rotation relative to a camera system is presented, thereby significantly improving results derived from poseNet by applying analysis of optical flow in a frame to frame comparison. The human body in-place rotating in T-pose is thereby aligned in the center, applying object tracking methods to compensate for translations of the body movement. After 2D skeleton extraction, the optical flow is calculated for a region of interest (ROI) area aligned relative to the vertical skeleton joint representing the spine and compared frame by frame. To evaluate the eligibility of the clothing as a fundament for good feature, the local pixel homogeneity is taken into consideration to restrict the optical flow to heterogeneous regions with distinctive features like imprint patterns, buttons or buckles besides local illumination changes. Based on the mean optical flow with a coarse approximation of the axial body shape as ellipsis, an accuracy between 0.1° and 2.0° by a target rotation of 10° for orientation-estimation is achieved on a frame-toframe comparison evaluated and validated on both, Computer Generated Imagery (CGI) renderings and real-world videos of people wearing clothing of varying feature appropriateness.en
dc.format9 s.cs
dc.format.mimetypeapplication/pdf
dc.identifier.citationWSCG 2020: full papers proceedings: 28th International Conference in Central Europe on Computer Graphics, Visualization and Computer Vision, p. 39-47.en
dc.identifier.doihttps://doi.org/10.24132/CSRN.2020.3001.5
dc.identifier.isbn978-80-86943-35-0
dc.identifier.issn2464–4617 (print)
dc.identifier.issn2464–4625 (CD-ROM)
dc.identifier.urihttp://wscg.zcu.cz/WSCG2020/2020-CSRN-3001.pdf
dc.identifier.urihttp://hdl.handle.net/11025/38449
dc.language.isoenen
dc.publisherVáclav Skala - UNION Agencycs
dc.relation.ispartofseriesWSCG 2020: full papers proceedingsen
dc.rights© Václav Skala - UNION Agencycs
dc.rights.accessopenAccessen
dc.subjectsledování objektůcs
dc.subjectorientace-odhadcs
dc.subjectoptický tokcs
dc.subjectnarovnání obrazucs
dc.subjectextrakce lidské kostrycs
dc.subjectodhad lidské pozicecs
dc.subjecthomogenita pixelůcs
dc.subject.translatedobject trackingen
dc.subject.translatedorientation-estimationen
dc.subject.translatedoptical flowen
dc.subject.translatedimage alignmenten
dc.subject.translatedhuman skeleton extractionen
dc.subject.translatedhuman pose estimationen
dc.subject.translatedpixel homogeneityen
dc.titleHybrid Approach for Orientation-Estimation of Rotating Humans in Video Frames Acquired by Stationary Monocular Cameraen
dc.typeconferenceObjecten
dc.typekonferenční příspěvekcs
dc.type.statusPeer-revieweden
dc.type.versionpublishedVersionen

Files

Original bundle
Showing 1 - 1 out of 1 results
No Thumbnail Available
Name:
E83.pdf
Size:
1.48 MB
Format:
Adobe Portable Document Format
Description:
Plný text
Download
License bundle
Showing 1 - 1 out of 1 results
No Thumbnail Available
Name:
license.txt
Size:
1.71 KB
Format:
Item-specific license agreed upon to submission
Description:
Download

Collections

WSCG 2020: Full Papers Proceedings
 OPEN License Selector